Advertisement

A Common Spatial Pattern Approach for Classification of Mental Counting and Motor Execution EEG

  • Purvi Goel
  • Raviraj Joshi
  • Mriganka Sur
  • Hema A. Murthy
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11278)

Abstract

A Brain Computer Interface (BCI) as a medium of communication is convenient for people with severe motor disabilities. Although there are a number of different BCIs, the focus of this paper is on Electroencephalography (EEG) as a means of human computer interaction. Motor imagery and mental arithmetic are the most popular techniques used to modulate brain waves that can be used to control devices. We show that it is possible to define different mental states using real fist rotation and imagined reverse counting. While people have already investigated left fist rotation and right fist rotation for dual state BCI, we intend to define a new state using mental reverse counting. We use Common Spatial Pattern (CSP) approach for feature extraction to distinguish between these states. CSP has been prominently used in the context of motor imagery task, we define its applicability for the distinction between motor execution and mental counting. CSP features are evaluated using classifiers like GMM, SVM, and GMM-UBM. GMM-UBM using data filtered through the beta band (13–30 Hz) gives the best performance.

Keywords

Brain computer interface Electroencephalography Motor execution Mental counting Common spatial pattern Gaussian mixture model Support vector machine 

References

  1. 1.
    The geodesic sensor net. https://www.egi.com/research-division/geodesic-sensor-net. Accessed 09 Jan 2018
  2. 2.
    Anderson, C.W., Sijercic, Z.: Classification of EEG signals from four subjects during five mental tasks. In: Solving Engineering Problems with Neural Networks: Proceedings of the Conference on Engineering Applications in Neural Networks (EANN 1996), Turkey, pp. 407–414 (1996)Google Scholar
  3. 3.
    Ang, K.K., Chin, Z.Y., Zhang, H., Guan, C.: Filter bank common spatial pattern (FBCSP) in brain-computer interface. In: IEEE International Joint Conference on Neural Networks, IJCNN 2008, (IEEE World Congress on Computational Intelligence), pp. 2390–2397. IEEE (2008)Google Scholar
  4. 4.
    Belhadj, S.A., Benmoussat, N., Della Krachai, M.: CSP features extraction and FLDA classification of EEG-based motor imagery for brain-computer interaction. In: 2015 4th International Conference on Electrical Engineering (ICEE), pp. 1–6. IEEE (2015)Google Scholar
  5. 5.
    Choi, K., Cichocki, A.: Control of a wheelchair by motor imagery in real time. In: Intelligent Data Engineering and Automated Learning - IDEAL 2008, 9th International Conference, Daejeon, South Korea, 2–5 November 2008, Proceedings, pp. 330–337 (2008).  https://doi.org/10.1007/978-3-540-88906-9-42
  6. 6.
    Guo, L., Wu, Y., Zhao, L., Cao, T., Yan, W., Shen, X.: Classification of mental task from EEG signals using immune feature weighted support vector machines. IEEE Trans. Magn. 47(5), 866–869 (2011)CrossRefGoogle Scholar
  7. 7.
    Keerthi, S.S., Lin, C.J.: Asymptotic behaviors of support vector machines with Gaussian kernel. Neural Comput. 15(7), 1667–1689 (2003)CrossRefGoogle Scholar
  8. 8.
    Keirn, Z.A., Aunon, J.I.: A new mode of communication between man and his surroundings. IEEE Trans. Biomed. Eng. 37(12), 1209–1214 (1990)CrossRefGoogle Scholar
  9. 9.
    Koles, Z.J., Lazar, M.S., Zhou, S.Z.: Spatial patterns underlying population differences in the background EEG. Brain Topogr. 2(4), 275–284 (1990)CrossRefGoogle Scholar
  10. 10.
    LaFleur, K., Cassady, K., Doud, A., Shades, K., Rogin, E., He, B.: Quadcopter control in three-dimensional space using a noninvasive motor imagery-based brain-computer interface. J. Neural Eng. 10(4), 046003 (2013)CrossRefGoogle Scholar
  11. 11.
    Leeb, R., Friedman, D., Müller-Putz, G.R., Scherer, R., Slater, M., Pfurtscheller, G.: Self-paced (asynchronous) BCI control of a wheelchair in virtual environments: a case study with a tetraplegic. Comp. Int. Neurosci. (2007).  https://doi.org/10.1155/2007/79642CrossRefGoogle Scholar
  12. 12.
    Li, Y., et al.: An EEG-based BCI system for 2-D cursor control by combining mu/beta rhythm and P300 potential. IEEE Trans. Biomed. Eng. 57(10), 2495–2505 (2010).  https://doi.org/10.1109/TBME.2010.2055564CrossRefGoogle Scholar
  13. 13.
    Liang, N., Saratchandran, P., Huang, G., Sundararajan, N.: Classification of mental tasks from EEG signals using extreme learning machine. Int. J. Neural Syst. 16(1), 29–38 (2006).  https://doi.org/10.1142/S0129065706000482CrossRefGoogle Scholar
  14. 14.
    Lotte, F., Congedo, M., Lécuyer, A., Lamarche, F., Arnaldi, B.: A review of classification algorithms for EEG-based brain-computer interfaces. J. Neural Eng. 4(2), R1 (2007)CrossRefGoogle Scholar
  15. 15.
    Lotte, F., Guan, C.: Regularizing common spatial patterns to improve BCI designs: unified theory and new algorithms. IEEE Trans. Biomed. Eng. 58(2), 355–362 (2011)CrossRefGoogle Scholar
  16. 16.
    Mahmood, A., Zainab, R., Ahmad, R.B., Saeed, M., Kamboh, A.M.: Classification of multi-class motor imagery EEG using four band common spatial pattern. In: 2017 39th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), Jeju Island, South Korea, 11–15 July 2017, pp. 1034–1037 (2017).  https://doi.org/10.1109/EMBC.2017.8037003
  17. 17.
    Osaka, M.: Peak alpha frequency of EEG during a mental task: task difficulty and hemispheric differences. Psychophysiology 21(1), 101–105 (1984)CrossRefGoogle Scholar
  18. 18.
    Özmen, N.G., Ktü, L.G.: Discrimination between mental and motor tasks of EEG signals using different classification methods. In: 2011 International Symposium on Innovations in Intelligent Systems and Applications (INISTA), pp. 143–147. IEEE (2011)Google Scholar
  19. 19.
    Ozmen, N.G., Gumusel, L.: Classification of real and imaginary hand movements for a BCI design. In: 2013 36th International Conference on Telecommunications and Signal Processing (TSP), pp. 607–611. IEEE (2013)Google Scholar
  20. 20.
    Pfurtscheller, G., Kalcher, J., Neuper, C., Flotzinger, D., Pregenzer, M.: On-line EEG classification during externally-paced hand movements using a neural network-based classifier. Electroencephalogr. Clin. Neurophysiol. 99(5), 416–425 (1996)CrossRefGoogle Scholar
  21. 21.
    Ramoser, H., Muller-Gerking, J., Pfurtscheller, G.: Optimal spatial filtering of single trial EEG during imagined hand movement. IEEE Trans. Rehabil. Eng. 8(4), 441–446 (2000)CrossRefGoogle Scholar
  22. 22.
    Reynolds, D.A., Quatieri, T.F., Dunn, R.B.: Speaker verification using adapted Gaussian mixture models. Digit. Signal Process. 10(1–3), 19–41 (2000)CrossRefGoogle Scholar
  23. 23.
    Wang, J., Feng, Z., Lu, N.: Feature extraction by common spatial pattern in frequency domain for motor imagery tasks classification. In: 2017 29th Chinese Control and Decision Conference (CCDC), pp. 5883–5888. IEEE (2017)Google Scholar
  24. 24.
    Wang, Y., Gao, S., Gao, X.: Common spatial pattern method for channel selection in motor imagery based brain-computer interface. In: 27th Annual International Conference of the Engineering in Medicine and Biology Society, IEEE-EMBS 2005, pp. 5392–5395. IEEE (2006)Google Scholar
  25. 25.
    Zhiwei, L., Minfen, S.: Classification of mental task EEG signals using wavelet packet entropy and SVM. In: 8th International Conference on Electronic Measurement and Instruments, ICEMI 2007, pp. 3–906. IEEE (2007)Google Scholar

Copyright information

© Springer Nature Switzerland AG 2018

Authors and Affiliations

  • Purvi Goel
    • 1
  • Raviraj Joshi
    • 1
  • Mriganka Sur
    • 2
  • Hema A. Murthy
    • 1
  1. 1.Department of Computer Science and EngineeringIndian Institute of Technology MadrasChennaiIndia
  2. 2.Department of Brain and Cognitive SciencesMassachusetts Institute of TechnologyCambridgeUSA

Personalised recommendations